1. Read in the gapminder_clean.csv data as a pandas DataFrame.

2. Filter the data to include only rows where Year is 1962 and then make a scatter plot comparing 'CO2 emissions (metric tons per capita)' and gdpPercap for the filtered data.

3. On the filtered data, calculate the pearson correlation of 'CO2 emissions (metric tons per capita)' and gdpPercap. What is the Pearson R value and associated p value?

Pearson R value: 0.9260816725019472; associated p value: 1.1286792210038754e-46

4.On the unfiltered data, answer "In what year is the correlation between 'CO2 emissions (metric tons per capita)' and gdpPercap the strongest?" Filter the dataset to that year for the next step...

In 1967, the correlation between 'CO2 emissions (metric tons per capita)' and gdpPercap is the strongest.

5.Using plotly, create an interactive scatter plot comparing 'CO2 emissions (metric tons per capita)' and gdpPercap, where the point size is determined by pop (population) and the color is determined by the continent.

What is the relationship between continent and 'Energy use (kg of oil equivalent per capita)'?

Africa has the least energy use while Oceania has the most

Is there a significant difference between Europe and Asia with respect to 'Imports of goods and services (% of GDP)' in the years after 1990?

No significant difference.

What is the country (or countries) that has the highest 'Population density (people per sq. km of land area)' across all years? (i.e., which country has the highest average ranking in this category across each time point in the dataset?)

Macao SAR, China and Monaco

What country (or countries) has shown the greatest increase in 'Life expectancy at birth, total (years)' since 1962?

Maldives